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Record W2010655391 · doi:10.1177/0733464810382526

Integrating the Ecological Approach in Disease Prevention and Health Promotion Programs for Older Adults

2010· article· en· W2010655391 on OpenAlexaffabout
Lucie Richard, Lise Gauvin, Francine Ducharme, Marie‐Éve Leblanc, Maryse Trudel

Bibliographic record

VenueJournal of Applied Gerontology · 2010
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHealth promotionPsychological interventionOddsPromotion (chess)Public healthGerontologyPsychologyGraduate degreePublic relationsPoliticsMedical educationNursingMedicinePolitical science

Abstract

fetched live from OpenAlex

The objective of this study was to identify contextual factors influencing the degree of integration of the ecological approach in disease prevention and health promotion (DPHP) programming initiatives for older adults in two public health organizations in Québec, Canada. A case study design was used and two organizations presenting contrasting profiles in the degree of integration of the approach in their DPHP programming for older adults were selected. Face-to-face interviews with professionals and managers and archival analysis were conducted. Several factors emerged as constraining the integration of the approach in both organizations, including the lack of data showing the effectiveness of DPHP interventions for older adults and the presence of macro-contextual political factors at odds with the ideology of DPHP. Resources and partnerships with academic milieus emerged as key factors distinguishing the two organizations. These results provide increased understanding of conditions required for planning DPHP programs for older adults.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.313
Threshold uncertainty score0.622

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0080.004
Scholarly communication0.0030.001
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.398
GPT teacher head0.599
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2010
Admission routes2
Has abstractyes

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